Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 24811 - 24840 of 63967

Full-Text Articles in Computer Sciences

Monte Carlo Simulations Of Coupled Transient Seepage Flow And Compressive Stress In Levees, Fred Thomas Tracy, Ghada Ellithy, Jodi L. Ryder, Martin T. Schultz, Benjamin R. Breland, T. Chris Massey, Maureen K. Corcoran Jan 2020

Monte Carlo Simulations Of Coupled Transient Seepage Flow And Compressive Stress In Levees, Fred Thomas Tracy, Ghada Ellithy, Jodi L. Ryder, Martin T. Schultz, Benjamin R. Breland, T. Chris Massey, Maureen K. Corcoran

Publications

The purpose of this research is to compare the results from two different computer programs of flow analyses of two levees at Port Arthur, Texas where rising water of a flood from Hurricane Ike occurred on the levees. The first program (Program 1) is a two-dimensional (2-D) transient finite element program that couples the conservation of mass flow equation with accompanying hydraulic boundary conditions with the conservation of force equations with accompanying x and y displacement and force boundary conditions, thus yielding total head, x displacement, and y displacement as unknowns at each finite element node. The second program (Program …

Go to article

Coverage Guided Differential Adversarial Testing Of Deep Learning Systems, Jianmin Guo, Houbing Song, Yue Zhao, Yu Jiang Jan 2020

Coverage Guided Differential Adversarial Testing Of Deep Learning Systems, Jianmin Guo, Houbing Song, Yue Zhao, Yu Jiang

Publications

Deep learning is increasingly applied to safety-critical application domains such as autonomous cars and medical devices. It is of significant importance to ensure their reliability and robustness. In this paper, we propose DLFuzz, the coverage guided differential adversarial testing framework to guide deep learing systems exposing incorrect behaviors. DLFuzz keeps minutely mutating the input to maximize the neuron coverage and the prediction difference between the original input and the mutated input, without manual labeling effort or cross-referencing oracles from other systems with the same functionality. We also design multiple novel strategies for neuron selection to improve the neuron coverage. The …

Go to article

Algorithms For Inferring Multiple Microbial Networks, Sahar Tavakoli Jan 2020

Algorithms For Inferring Multiple Microbial Networks, Sahar Tavakoli

Electronic Theses and Dissertations, 2020-2023

The interactions among the constituent members of a microbial community play a major role in determining the overall behavior of the community and the abundance levels of its members. These interactions can be modeled using a network whose nodes represent microbial taxa and edges represent pairwise interactions. A microbial network is a weighted graph that is constructed from a sample-taxa count matrix and can be used to model co-occurrences and/or interactions of the constituent members of a microbial community. The nodes in this graph represent microbial taxa and the edges represent pairwise associations amongst these taxa. A microbial network is …

Go to article

3d Localization Of Defects In Facility Inspections, Nicholas Califano Jan 2020

3d Localization Of Defects In Facility Inspections, Nicholas Califano

Electronic Theses and Dissertations, 2020-2023

Wind tunnels are crucial facilities that support the aerospace industry. However, these facilities are large, complex, and pose unique maintenance and inspection requirements. Manual inspections to identify defects such as cracks, missing fasteners, leaks, and foreign objects are important but labor and schedule intensive. The goal of this thesis is to utilize small Unmanned Aircraft Systems with onboard cameras and computer vision-based analysis to automate the inspection of the interior and exterior of NASA's critical wind tunnel facilities. Missing fasteners are detected as the defect class, and existing fasteners are detected to provide potential future missing fastener sites for preventative …

Go to article

On Patching Learning Discrepancies In Neural Network Training, Mohamed Elfeki Jan 2020

On Patching Learning Discrepancies In Neural Network Training, Mohamed Elfeki

Electronic Theses and Dissertations, 2020-2023

Neural network's ability to model data patterns proved to be immensely useful in a plethora of practical applications. However, using the physical world's data can be problematic since it is often cluttered, crowded with scattered insignificant patterns, contain unusual compositions, and widely infiltrated with biases and imbalances. Consequently, training a neural network to find meaningful patterns in seas of chaotic data points becomes virtually as hard as finding a needle in a haystack. Specifically, attempting to simulate real-world multi-modal noisy distributions with high precision leads the network to learn an ill-informed inference distribution. In this work, we discuss four techniques …

Go to article

The Dollar General: Continuous Custom Gesture Recognition Techniques At Everyday Low Prices, Eugene Taranta Jan 2020

The Dollar General: Continuous Custom Gesture Recognition Techniques At Everyday Low Prices, Eugene Taranta

Electronic Theses and Dissertations, 2020-2023

Humans use gestures to emphasize ideas and disseminate information. Their importance is apparent in how we continuously augment social interactions with motion—gesticulating in harmony with nearly every utterance to ensure observers understand that which we wish to communicate, and their relevance has not escaped the HCI community's attention. For almost as long as computers have been able to sample human motion at the user interface boundary, software systems have been made to understand gestures as command metaphors. Customization, in particular, has great potential to improve user experience, whereby users map specific gestures to specific software functions. However, custom gesture recognition …

Go to article

Towards Large-Scale And Robust Code Authorship Identification With Deep Feature Learning, Mohammed Abuhamad Jan 2020

Towards Large-Scale And Robust Code Authorship Identification With Deep Feature Learning, Mohammed Abuhamad

Electronic Theses and Dissertations, 2020-2023

Successful software authorship identification has both software forensics applications and privacy implications. However, the process requires an efficient extraction of quality authorship attributes. The extraction of such attributes is very challenging due to several factors such as the variety of software formats, number of available samples, and possible obfuscation or adversarial manipulation. We focus on software authorship identification from three central perspectives: large-scale single-authored software, real-world multi-authored software, and the robustness assessment of code authorship identification methods against adversarial attacks. First, we propose DL-CAIS, a deep Learning-based approach for software authorship attribution, that facilitates large-scale, format-independent, language-oblivious, and obfuscation-resilient software …

Go to article

Thwarting Icmp Low-Rate Attacks Against Firewalls While Minimizing Legitimate Traffic Loss, Kadhim Hayawi, Zouheir Trabelsi, Safaa Zeidan, Mohammad Mehedy Masud Jan 2020

Thwarting Icmp Low-Rate Attacks Against Firewalls While Minimizing Legitimate Traffic Loss, Kadhim Hayawi, Zouheir Trabelsi, Safaa Zeidan, Mohammad Mehedy Masud

All Works

© 2013 IEEE. Low-rate distributed denial of service (LDDoS) attacks pose more challenging threats that disrupt network security devices and services. Such type of attacks is difficult to detect and mitigate. In LDDoS attacks, attacker uses low-volume of malicious traffic that looks alike legitimate traffic. Thus, it can enter the network in silence without any notice. However, it may have severe effect on disrupting network services, depleting system resources, and degrading network speed to a point considering them as one of the most damaging attack types. There are many types of LDDoS such as application server and ICMP error messages …

Go to article

Framework For Examination Of Software Quality Characteristics In Conflict: A Security And Usability Exemplar, Bilal Naqvi, Ahmed Seffah, Alain Abran Jan 2020

Framework For Examination Of Software Quality Characteristics In Conflict: A Security And Usability Exemplar, Bilal Naqvi, Ahmed Seffah, Alain Abran

All Works

© 2020, © 2020 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Standards and best practices for software quality guide on handling each quality characteristic individually, but not when two or more characteristics come into conflict such as security and usability. The objectives of this paper are twofold: (a) to argue on the importance of handling the conflicts between quality characteristics in general; (b) to formulate a framework for conflict examination of the software quality characteristics, we do so while considering the specific case of security and usability. In line with the …

Go to article

Analyzing And Detecting Internet Of Things Malware Using Residual Static Graph- And String-Based Artifacts, Hisham Alasmary Jan 2020

Analyzing And Detecting Internet Of Things Malware Using Residual Static Graph- And String-Based Artifacts, Hisham Alasmary

Electronic Theses and Dissertations, 2020-2023

Recently, the Internet of Things (IoT) has become wider and adopted many features from social networks and mainly uses sensing devices technologies, causing a rapid increase in production and adoption. However, security and privacy are serious threats that users usually take precautions to protect their devices and information. Thus, understanding the security shortcomings at first stage will educate IoT users to protect their connected things. Understanding IoT software through analysis, comparison (with other types of malware), and detection (from benign IoT) is an essential problem to mitigate security threats. We focus on two central perspectives, the graph and string representations …

Go to article

Open-Ended Search Through Minimal Criterion Coevolution, Jonathan Brant Jan 2020

Open-Ended Search Through Minimal Criterion Coevolution, Jonathan Brant

Electronic Theses and Dissertations, 2020-2023

Search processes guided by objectives are ubiquitous in machine learning. They iteratively reward artifacts based on their proximity to an optimization target, and terminate upon solution space convergence. Some recent studies take a different approach, capitalizing on the disconnect between mainstream methods in artificial intelligence and the field's biological inspirations. Natural evolution has an unparalleled propensity for generating well-adapted artifacts, but these artifacts are decidedly non-convergent. This new class of non-objective algorithms induce a divergent search by rewarding solutions according to their novelty with respect to prior discoveries. While the diversity of resulting innovations exhibit marked parallels to natural evolution, …

Go to article

Interactions Between Humans, Virtual Agent Characters And Virtual Avatars, Tamara Griffith Jan 2020

Interactions Between Humans, Virtual Agent Characters And Virtual Avatars, Tamara Griffith

Electronic Theses and Dissertations, 2020-2023

Simulations allow people to experience events as if they were happening in the real world in a way that is safer and less expensive than live training. Despite improvements in realism in simulated environments, one area that still presents a challenge is interpersonal interactions. The subtleties of what makes an interaction rich are difficult to define. We may never fully understand the complexity of human interchanges, however there is value in building on existing research into how individuals react to virtual characters to inform future investments. Virtual characters can either be automated through computational processes, referred to as agents, or …

Go to article

Video Content Understanding Using Text, Amir Mazaheri Jan 2020

Video Content Understanding Using Text, Amir Mazaheri

Electronic Theses and Dissertations, 2020-2023

The rise of the social media and video streaming industry provided us a plethora of videos and their corresponding descriptive information in the form of concepts (words) and textual video captions. Due to the mass amount of available videos and the textual data, today is the best time ever to study the Computer Vision and Machine Learning problems related to videos and text. In this dissertation, we tackle multiple problems associated with the joint understanding of videos and text. We first address the task of multi-concept video retrieval, where the input is a set of words as concepts, and the …

Go to article

Explore And Design Novel Structures For More Efficient And Better Deep Convolutional Neural Networks, Min Wang Jan 2020

Explore And Design Novel Structures For More Efficient And Better Deep Convolutional Neural Networks, Min Wang

Electronic Theses and Dissertations, 2020-2023

Deep Convolutional Neural Networks have achieved remarkable performance on visual recognition problems, and have been extensively adopted in real-world applications, such as Apple's Face ID security system, autonomous driving cars, and automatic image tagging in online album services. One major concern in the development of CNNs is that their computational complexity grows along with the increase in their accuracy. Therefore, there is a continuous demand to find the right balance between accuracy and complexity in the design of CNN models. This dissertation focuses on designing various novel structures to enhance the performance of CNNs and their efficiency. Our efforts fall …

Go to article

Differential Recurrent Neural Networks For Human Activity Recognition, Naifan Zhuang Jan 2020

Differential Recurrent Neural Networks For Human Activity Recognition, Naifan Zhuang

Electronic Theses and Dissertations, 2020-2023

Human activity recognition has been an active research area in recent years. The difficulty of this problem lies in the complex dynamical motion patterns embedded through the sequential frames. The Long Short-Term Memory (LSTM) recurrent neural network is capable of processing complex sequential information since it utilizes special gating schemes for learning representations from long input sequences. It has the potential to model various time-series data, where the current hidden state has to be considered in the context of the past hidden states. Unfortunately, the conventional LSTMs do not consider the impact of spatio-temporal dynamics corresponding to the given salient …

Go to article

Learning Transferable Representations For Visual Recognition, Yang Zhang Jan 2020

Learning Transferable Representations For Visual Recognition, Yang Zhang

Electronic Theses and Dissertations, 2020-2023

In the last half-decade, a new renaissance of machine learning originates from the applications of convolutional neural networks to visual recognition tasks. It is believed that a combination of big curated data and novel deep learning techniques can lead to unprecedented results. However, the increasingly large training data is still a drop in the ocean compared with scenarios in the wild. In this literature, we focus on learning transferable representation in the neural networks to ensure the models stay robust, even given different data distributions. We present three exemplar topics in three chapters, respectively: zero-shot learning, domain adaptation, and generalizable …

Go to article

Navigating Immersive And Interactive Vr Environments With Connected 360° Panoramas, Samuel Cosgrove Jan 2020

Navigating Immersive And Interactive Vr Environments With Connected 360° Panoramas, Samuel Cosgrove

Electronic Theses and Dissertations, 2020-2023

Emerging research is expanding the idea of using 360-degree spherical panoramas of real-world environments for use in "360 VR" experiences beyond video and image viewing. However, most of these experiences are strictly guided, with few opportunities for interaction or exploration. There is a desire to develop experiences with cohesive virtual environments created with 360 VR that allow for choice in navigation, versus scripted experiences with limited interaction. Unlike standard VR with the freedom of synthetic graphics, there are challenges in designing appropriate user interfaces (UIs) for 360 VR navigation within the limitations of fixed assets. To tackle this gap, we …

Go to article

Computational Methods For Discovery And Analysis Of Rna Structural Motifs, Shahidul Islam Jan 2020

Computational Methods For Discovery And Analysis Of Rna Structural Motifs, Shahidul Islam

Electronic Theses and Dissertations, 2020-2023

Understanding the 3D structural properties of RNAs will play a critical role in identifying their functional characteristics and designing new RNAs for RNA-based therapeutics and nanotechnology. In an attempt to achieve a better insight into RNAs, biochemical experiments have been conducted to produce data with positional details of atoms in RNA structures. This data has created opportunities for applying computational analysis to solve various biological problems. In this dissertation, we have addressed annotation issues of base-pairing interactions in the low-resolution structure data and presented new methods to analyze RNA structural motifs. Annotating base-pairing interactions is one of the critical steps …

Go to article

Towards Scalable Network Traffic Measurement With Sketches, Rhongho Jang Jan 2020

Towards Scalable Network Traffic Measurement With Sketches, Rhongho Jang

Electronic Theses and Dissertations, 2020-2023

Driven by the ever-increasing data volume through the Internet, the per-port speed of network devices reached 400 Gbps, and high-end switches are capable of processing 25.6 Tbps of network traffic. To improve the efficiency and security of the network, network traffic measurement becomes more important than ever. For fast and accurate traffic measurement, managing an accurate working set of active flows (WSAF) at line rates is a key challenge. WSAF is usually located in high-speed but expensive memories, such as TCAM or SRAM, and thus their capacity is quite limited. To scale up the per-flow measurement, we pursue three thrusts. …

Go to article

Design Of Ternary Operations Utilizing Flow-Based Computing, James Pyrich Jan 2020

Design Of Ternary Operations Utilizing Flow-Based Computing, James Pyrich

Electronic Theses and Dissertations, 2020-2023

The development of algorithms and circuit designs that exploit devices that have the ability to persist multiple values will lead to alternative technologies to overcome the issues caused by the end of Dennard scaling and slowing of Moore's Law. Flow-based designs have been used to develop binary adders and multipliers. Data stored on non-volatile memristors are used to direct the flow of current through nanowires arranged in a crossbar. The algorithmic design of the flow-based crossbar is fast, compact, and efficient. In this paper, we seek to automate the discovery of flow-based designs of ternary circuits utilizing memristive crossbars.

Go to article

Towards Robust Artificial Intelligence Systems, Sunny Raj Jan 2020

Towards Robust Artificial Intelligence Systems, Sunny Raj

Electronic Theses and Dissertations, 2020-2023

Adoption of deep neural networks (DNNs) into safety-critical and high-assurance systems has been hindered by the inability of DNNs to handle adversarial and out-of-distribution input. State-of-the-art DNNs misclassify adversarial input and give high confidence output for out-of-distribution input. We attempt to solve this problem by employing two approaches, first, by detecting adversarial input and, second, by developing a confidence metric that can indicate when a DNN system has reached its limits and is not performing to the desired specifications. The effectiveness of our method at detecting adversarial input is demonstrated against the popular DeepFool adversarial image generation method. On a …

Go to article

The Susceptibility Of Deep Neural Networks To Natural Perturbations, Mesut Ozdag Jan 2020

The Susceptibility Of Deep Neural Networks To Natural Perturbations, Mesut Ozdag

Electronic Theses and Dissertations, 2020-2023

Deep learning systems have achieved great success in various types of applications in recent years. They are increasingly being adopted for safety-critical tasks such as face recognition, surveillance systems, speech recognition, and autonomous driving. On the other hand, it has been found that deep neural networks (DNNs) can easily be fooled by adversarial input samples. These imperceptible perturbations on images can lead any machine learning system to misclassify the objects with high confidence. Furthermore, they can be almost indistinguishable to a human observer. These systems can also be exposed to adverse weather conditions such as fog, rain, and snow. This …

Go to article

Automated Process Of Quantifying Scientific Images Using Fiji, Olivia Casimir Jan 2020

Automated Process Of Quantifying Scientific Images Using Fiji, Olivia Casimir

Summer Community of Scholars Posters (RCEU and HCR Combined Programs)

No abstract provided.

Go to article

Creating And Deploying Automated Software Test Procedures With Regression Testing, Austen Seidler Jan 2020

Creating And Deploying Automated Software Test Procedures With Regression Testing, Austen Seidler

Summer Community of Scholars Posters (RCEU and HCR Combined Programs)

No abstract provided.

Go to article

Gaming Lan Setup With Local And Remote Access And Downloads, Ethelyn Tran Jan 2020

Gaming Lan Setup With Local And Remote Access And Downloads, Ethelyn Tran

Williams Honors College, Honors Research Projects

The Gaming LAN Setup project aims to design and implement a basic functioning, hardened network that could be utilized locally and remotely to allow users access to respective servers for the option to host a session or join. Users will have the ability to securely log into the internal network to download files via a web interface. The network allows the designated user to take a management position in order to perform basic penetration testing and discover vulnerabilities through various scans to maintain the network

Go to article

Development And Field Testing Of A Mall For Filipino With A Reusable Framework For Mobile-Based Drills, Jenilyn Agapito, Dominique Marie Antoinette Manahan, Ma. Monica L. Moreno, Jose Isidro Beraquit, Ingrid Yvonne Herras, Kevin Arnel C. Mora, Johanna Marion R. Torres, Ma. Mercedes T. Rodrigo Jan 2020

Development And Field Testing Of A Mall For Filipino With A Reusable Framework For Mobile-Based Drills, Jenilyn Agapito, Dominique Marie Antoinette Manahan, Ma. Monica L. Moreno, Jose Isidro Beraquit, Ingrid Yvonne Herras, Kevin Arnel C. Mora, Johanna Marion R. Torres, Ma. Mercedes T. Rodrigo

Department of Information Systems & Computer Science Faculty Publications

This paper describes the development and field testing of Ibigkas! Filipino, a mobile game that exercises learners’ fluency in identifying synonyms (kasingkahulugan) and antonyms (kasalungat) in the Filipino language. Twenty-four students from Grades 4, 5, and 6 were invited to play and answer comprehension tests to determine whether the game helped them improve their understanding of the content. Self-report questionnaires assessed the extent to which they enjoyed it. Additionally, three teachers were invited to a focus group discussion (FGD) to gather their insights about the game and how they may use it in their classes. Self-report feedback from students showed …

Go to article

Troika Generative Adversarial Network (T-Gan): A Synthetic Image Generator That Improves Neural Network Training For Handwriting Classification, Joe Anthony M. Milan, Proceso L. Fernandez Jr Jan 2020

Troika Generative Adversarial Network (T-Gan): A Synthetic Image Generator That Improves Neural Network Training For Handwriting Classification, Joe Anthony M. Milan, Proceso L. Fernandez Jr

Department of Information Systems & Computer Science Faculty Publications

Training an artificial neural network for handwriting classification requires a sufficiently sized annotated dataset in order to avoid overfitting. In the absence of sufficient instances, data augmentation techniques are normally considered. In this paper, we propose the troika generative adversarial network (T-GAN) for data augmentation to address the scarcity of publicly labeled handwriting datasets. T-GAN has three generator subnetworks architectured to have some weight-sharing in order to learn the joint distribution from three specific domains. We used T-GAN to augment the data from a subset of the IAM Handwriting Database. We then compared this with other data augmentation techniques by …

Go to article

Predicting Stag And Hare Hunting Behaviors Using Hidden Markov Model, Rex Bringula, Ma. Mercedes T. Rodrigo Jan 2020

Predicting Stag And Hare Hunting Behaviors Using Hidden Markov Model, Rex Bringula, Ma. Mercedes T. Rodrigo

Department of Information Systems & Computer Science Faculty Publications

In this paper, we used Hidden Markov Model (HMM) to describe the gaming behaviors of students and whether they will exhibit “stag” or “hare” hunting behavior in a mobile game for mathematics learning. We found that there is a 99% probability that the students will stay either as stag or hare hunters. Our results also suggest that they would choose arithmetic problems involving addition. These game behaviors are not beneficial to learning because they are only exhibiting mathematical skills they already know. The results of the study show that stag and hare hunters have unique traits that separate the one …

Go to article

Data-Driven Personalized Applications In Networks, Chuankai An Jan 2020

Data-Driven Personalized Applications In Networks, Chuankai An

Dartmouth College Ph.D Dissertations

A network models relationships. For a network that either encodes or supports internal information sharing activities, a better understanding of the network may enable data-driven applications (e.g., social network based recommendation), and boost both descriptive and predictive modeling of information flow in itself. In a multi-faceted manner, we propose in this thesis to contribute to several challenges that arise in the development of personalized applications in the general area of information and networks: 1) articulation of new patterns (and associated metrics) for individual user behavior and network structure; 2) exploitation of new forms of feature vector representations derived from large …

Go to article

Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman Jan 2020

Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management, Nathaniel C. Crossman

Browse all Theses and Dissertations

In the last decade, the advent of social media and microblogging services have inevitably changed our world. These services produce vast amounts of streaming data, and one of the most important ways of analyzing and discovering interesting trends in the streaming data is through clustering. In clustering streaming data, it is desirable to perform a single pass over incoming data, such that we do not need to process old data again, and the clustering model should evolve over time not to lose any important feature statistics of the data. In this research, we have developed a new clustering system that …

Go to article

First Prev Next Last